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Optimizing Inter-server Communications by Exploiting Overlapping Communities in Online Social Networks

  • Jingya ZhouEmail author
  • Jianxi Fan
  • Baolei Cheng
  • Juncheng Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10048)

Abstract

As the rapid growth of online social networks (OSNs), inter-server communications are becoming an obstacle to scaling the storage systems of OSNs. To address the problem, network partitioning and data replication are two commonly used approaches. In this paper, we exploit the combination of both approaches simultaneously and propose a data placement scheme based on overlapping communities detection. The principle behind the proposed scheme is to co-locate frequently interactive users together as long as it brings positive traffic reduction and satisfies load constraint. We conduct trace-driven experiments and the results show that our scheme significantly reduces the inter-server communications as well as preserving good load balancing.

Keywords

Inter-server communications Online social networks Data placement Network partitioning Data replication 

References

  1. 1.
    Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: MSST, pp. 1–10 (2010)Google Scholar
  2. 2.
    Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. Oper. Syst. Rev. 44(2), 35–40 (2010)CrossRefGoogle Scholar
  3. 3.
    Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20, 359–392 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chen, H., Jin, H., Jin, N., Gu, T.: Minimizing inter-server communications by exploiting self-similarity in online social networks. In: ICNP, pp. 1–10 (2012)Google Scholar
  5. 5.
    Pujol, J.M., Erramilli, V., Siganos, G., Yang, X., Laoutaris, N., Chhabra, P., Rodriguez, P.: The little engine(s) that could: scaling online social networks. IEEE/ACM Trans. Netw. 20(4), 1162–1175 (2012)CrossRefGoogle Scholar
  6. 6.
    Liu, G., Shen, H., Chandler, H.: Selective data replication for online social networks with distributed datacenters. In: ICNP, pp. 1–10 (2013)Google Scholar
  7. 7.
    Zhou, J., Fan, J., Wang, J., Cheng, B., Jia, J.: Towards traffic minimization for data placement in online social networks. Concurrency and Computation: Practice and Experience, May 2016Google Scholar
  8. 8.
    Wilson, C., Sala, A., Puttaswamy, K.P.N., Zhao, B.Y.: Beyond social graphs: user interactions in online social networks and their implications. ACM Trans. Web 6, 17:1–17:31 (2012)CrossRefGoogle Scholar
  9. 9.
    Gjoka, M., Kurant, M., Butts, C.T., Markopoulou, A.: Walking in facebook: a case study of unbiased sampling of OSNs. In: INFOCOM, pp. 2498–2506 (2010)Google Scholar
  10. 10.
    Jiang, J., Wilson, C., Wang, X., Sha, W., Huang, P., Dai, Y., Zhao, B.Y.: Understanding latent interactions in online social networks. ACM Trans. Web 7, 18 (2013)CrossRefGoogle Scholar
  11. 11.
    Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.A.F.: Characterizing user behavior in online social networks. In: IMC, pp. 49–62 (2009)Google Scholar
  12. 12.
    Tran, D.A., Nguyen, K., Pham, C.: S-clone: socially-aware data replication for social networks. Comput. Netw. 56, 2001–2013 (2012)CrossRefGoogle Scholar
  13. 13.
    Yu, B., Pan, J.: Location-aware associated data placement for geo-distributed data-intensive applications. In: INFOCOM, pp. 603–611 (2015)Google Scholar
  14. 14.
    Tran, D.A., Zhang, T.: S-PUT: an EA-based framework for socially aware data partitioning. Comput. Netw. 75, 504–518 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jingya Zhou
    • 1
    • 2
    Email author
  • Jianxi Fan
    • 1
    • 2
  • Baolei Cheng
    • 1
    • 2
  • Juncheng Jia
    • 1
    • 2
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

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